Three Applications of Big Data in Logistics and Transportation

Technology is unlocking a domain of conceivable outcomes to capitalize on Big Data in the logistics industry. Here’s how logistics enterprises can succeed from a data-driven approach.

Efficient route and capacity planning

Logistics is no exception when it comes to data unlocking new opportunities across numerous industries. Beginning with a driver’s journey, big data analytics in logistics play an important part in dynamic route planning – a method of accommodating updates to weather, traffic, and orders. In this case, data collected from sensors within a truck, a weather report or similar is utilized to suggest the best possible route to drivers. According to research conducted in support of its recent 2016 Logistics Trend Radar, DHL thinks the impact of data-driven and autonomous supply chains provides an opportunity for “previously unimaginable levels of optimization” in manufacturing, logistics, warehousing, and last mile delivery that could become a reality in less than half a decade, despite high set-up costs deterring early adoption within the logistics industry.

UPS is a real-world case of big data logistics leading to big savings. After analyzing their data, UPS discovered that trucks turning left was causing a lot of delays, wasted fuel, and safety risk; hence, costing them a lot of money. In one of the posts titled “Why UPS drivers don’t turn left and you probably shouldn’t either”, UPS states that it now uses 10m gallons less fuel, emits 20,000 tonnes less carbon dioxide and delivers 350,000 more packages every year” (after switching to route optimization).

Similarly, an equivalent approach is employed for capacity planning, where a machinist can use predictive analytics to peek into their accessibility of assets and personnel to allocate resources where they’re most needed. Such measures prevent the same trucks heading in similar directions and ensure a better, more efficient operation.

Transparency and performance optimization

The use of data-accumulating sensors within vehicles ensures transparency around organizations. It has become certainly more practical to track the position and location of every vehicle to get an estimation about the expected delivery. This information can be further used to validate the reliability of the outperformers which helps with securing new contracts.

The objective should be for data to create a more transparent logistics industry where a group’s all-around performance is put beyond doubt. Again, for the most efficient groups – regardless of their size – it could prove a great signal of worth.

Demand prediction and dynamic pricing

Apart from the trucks and ships, big data and analytics can get down to the nitty-gritty, influencing matters to the core. Logistics is most likely to be the latest adopter of dynamic pricing: a model for market demand and asset availability to the state value of each delivery. Airlines are inching closer to dynamic pricing in hopes to maximize profits around key periods and increase conversion rates while driving incremental revenue increases.

There is even a possibility to utilize data in predicting manner for a time period. While great for the balance sheets and business planning, it’s in these areas where groups can also predict the circumstances that could upturn their operation around active periods. An analysis by World Economic Forum indicates that there is $1.5 trillion of value at stake for logistics players and a further $2.4 trillion worth of societal benefits as a result of digital transformation of the industry up until 2025. Industry stakeholders should now come forward and prioritize big data as an entry point to this growth.

Contact us to see how our reputed clients Digital Fleet and J.B. Hunt have experienced a greater efficiency in their logistics operations with the help of our big data and advanced analytics solutions.

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